Behavioral Validation of Individualized Low-Intensity Transcranial Electrical Stimulation (tES) Protocols

Abstract Large interindividual variability in the effects of low-intensity transcranial electrical stimulation (tES) considerably limits its potential for clinical applications. It has been recently proposed that individualizing stimulation dose by accounting for interindividual anatomic differences would reduce the variability in electric fields (E-fields) over the targeted cortical site and therefore produce more consistent behavioral outcomes. However, improvement in behavioral outcomes following individualized dose tES has never been compared with that of conventional fixed dose tES. In this study, we aimed to empirically evaluate the effect of individualized dose tES on behavior and further compare it with the effects of sham and fixed dose stimulations. We conducted a single-blinded, sham-controlled, repeated-measures study to examine the impact of transcranial direct current stimulation on motor learning and that of transcranial alternating current stimulation on the working memory of 42 healthy adult individuals. Each participant underwent three sessions of tES, receiving fixed dose, individualized dose, or sham stimulation over the targeted brain region for the entire behavioral task. Our results showed that the individualized dose reduced the variability in E-fields at the targeted cortical surfaces. However, there was no significant effect of tES on behavioral outcomes. We argue that although the stimulation dose and E-field intensity at the targeted cortical site are linearly correlated, the effect of E-fields on behavior seems to be more complex. Effective optimization of tES protocols warrants further research considering both neuroanatomical and functional aspects of behavior.


Significance Statement
Transcranial electrical stimulation (tES) has shown remarkable results in motor, cognitive, and clinical investigations.However, there is high variability in the efficacy of tES results with a small effect size and reproducibility.The E-field intensity variability at the targeted cortical site has been shown to contribute to a significant proportion of the variability in behavioral effects of tES.The variability in E-field intensity has recently been demonstrated to decrease with individualizing tES dosages.Consequently, it is widely assumed that uniform E-field intensity across individuals will lead to reduced variability in behavioral outcomes.In this study, we investigated the effect of individualized tES on motor and working memory behavior.Our results showed contradictory outcomes where individualized tES did not reduce the variability in behavioral outcomes.
tES holds immense potential in the treatment of neuropsychiatric illnesses (Uhlhaas et al., 2009;Kuo et al., 2017;Sreeraj et al., 2017;Elyamany et al., 2021).However, tES efficacy shows mixed results across studies with small effect sizes and poor reproducibility, limiting the use of tES in clinical applications.A meta-analysis on schizophrenia showed no direct effects of tDCS on positive and negative effects, with only a subgroup of patients showing improvement in symptoms with multiple tDCS interventions (Ciullo et al., 2021).Additionally, various behavioral studies on motor learning have shown poor reproducibility or nonreproducibility of tES interventions (Kang and Paik, 2011;Stagg et al., 2011;Ambrus et al., 2016;Ammann et al., 2016).A meta-analysis study by Senkowski et al. (2022) showed mixed effects of tDCS and tACS on working memory behavior, with single-session tDCS having no effect and multisession tDCS and tACS having moderate effects.
The large variability in the effects of tES has been attributed to the interindividual anatomic differences (Kim et al., 2014;Laakso et al., 2015) and different stimulation parameters (Li et al., 2015) used across studies.Integrated current flow modeling and neuroimaging indicate that a large proportion of this variability is because of differences in the direction of current flow (Rawji et al., 2018) and the distribution of electrics fields (E-fields) generated in the brain (Kasten et al., 2019;Evans et al., 2020).tES studies typically use a fixed dose of 1-2 mA to stimulate the subjects' brains without accounting for the interindividual anatomic differences.As a result, there is large variability in the E-fields generated on the cortical surface across individuals (Caulfield et al., 2022).
A recent study by Evans et al. (2020) showed a 100% reduction in variability in E-field intensity at the targeted cortical site by the dose individualization method.The proposed pipeline used current flow models to calculate the tDCS dose required to produce uniform E-fields across subjects.Assuming a direct linear relationship between the E-fields and physiological and behavioral effects, this approach was expected to reduce the variability in behavioral outcomes across individuals (Evans et al., 2020;Caulfield et al., 2022).However, this hypothesis has not been empirically validated.
In this single-blinded study, we empirically evaluated the effect of individualized tES dose on behavior.Further, we compared it with the effects of sham and fixed dose stimulation in healthy human adults.We targeted two brain regions: the left primary motor cortex to evaluate the effects of individualized tDCS on motor learning (Nitsche et al., 2003), and the right frontoparietal cortices to evaluate tACS on the working memory task (Violante et al., 2017), respectively.To achieve this, we adopted experimental parameters like behavioral task, electrode montage, and stimulation intensity from previous studies conducted by Nitsche et al. (2003) and Violante et al. (2017).We aimed to replicate the improvement in motor learning and working memory performance of individuals using tDCS and tACS, as shown by Nitsche et al. (2003) and Violante et al. (2017), respectively.We further hypothesized that individualized dose tES would produce a larger effect on the behavior because of less interindividual variability.

Study overview
Forty-two healthy adult volunteers were recruited for the study.The sample size was determined by statistical power analysis [Table 1] on the results of Nitsche et al. (2003) and Violante et al. (2017) for the tDCS and tACS experiments, respectively.The tDCS experiment had 23 participants (mean age ¼ 26.00 6 2.62 years; 16 females), while the tACS study had 36 participants (mean age ¼ 25.14 6 2.30 years; 25 females; Fig. 1).Seventeen subjects participated in both experiments.All participants were right handed (mean ¼ 78.45 6 24.95%) as evaluated by the Edinburgh The sample size required for the tDCS and tACS experiments is 23 and 36, respectively.For both analyses, we used an a priori, paired, two-tailed t test to compute required sample sizes using G*Power version 3.1.9.6 (http://www.gpower.hhu.de/).For the tDCS experiment, the expected effect size was chosen as the mean reaction time difference (ms) between the last learning block (block 5) for the sham and the anodal tDCS condition from the Nitsche et al. (2003) paper.Similarly, the expected effect size of the tACS experiment was the mean time difference (ms) between the sham and synchronous tACS conditions for the 2-back task at 6 Hz from Violante et al. (2017).Thus, based on the above studies, we expected a minimum difference in the means to be 55 and 75 ms between the conventional and individualized dose conditions for tDCS and tACS experiments.
handedness inventory (Oldfield, 1971).They all had a university or higher-level education, normal or corrected-to-normal vision, and no contraindications for tES (Kessler et al., 2012).Human subjects were recruited from the National Brain Research Centre (Manesar, India).The study conformed to the guidelines of the Declaration of Helsinki and was approved by the Institutional Human Ethics Committee of the National Brain Research Centre (NBRC; Manesar, India).All the participants signed a written informed consent before the commencement of the study.

Structural MRIs
Structural T1-weighted MR images were obtained for each participant.We had access to the MRI scans of 19 participants in the NBRC database in neuroimaging informatics technology initiative (NIFTI) format.The data were obtained from an Achieva 3.0 T MRI scanner (Philips) with the following parameters: TR ¼ 8.39 ms; TE ¼ shortest; FOV ¼ 250 Â 230.16 Â 170 mm; flip angle ¼ 8°; voxel size ¼ 1 mm 3 ; scan duration ¼ 5 min, 28.5 s.The other 23 scans were acquired in a 3 T MAGNETOM Prisma MRI scanner (Siemens).Data were obtained using the following protocol: TR ¼ 2300 ms; TE ¼ 2.33 ms; FOV ¼ 240 mm; voxel size ¼ 0.9 mm 3 ; flip angle ¼ 8°; scan duration ¼ 5 min, 21 s.The raw MR images were saved in the digital imaging and communications in medicine (DICOM) format and converted through MRIcroGL 1.2.20211006Â 86-64 FPC to NIFTI format.

Current flow modeling
We used Realistic Volumetric Approach to Simulate Transcranial Electric Stimulation (ROAST) and SPM12 for current flow modeling and E-fields extraction (Huang et al., 2013(Huang et al., , 2019)).Further, we adapted the pipeline devised by Evans et al. (2020; https://github.com/caryse/tDCS_dosecontrol) to compute the individualized dose and extract E-field intensities from the target site (Evans et al., 2020).The method uses current flow models coupled with neuroimaging to calculate the tES intensities.Individualization accounted for the interindividual differences in anatomic characteristics and delivered a uniform current to the targeted cortical site across individuals.

Regions of interest
To access the E-field intensity at the target regions-left primary motor cortex (M1; for the tDCS experiment) and frontoparietal cortices (for the tACS experiment)-eigen variates were extracted from spherical regions of interest (ROIs) with a 5 mm radius using SPM12.The MNI coordinates for M1 (MNI: À38, À20, 50) were based on the activation likelihood estimation results by Eickhoff et al. (2009).Similarly, we used ROIs in the right middle frontal gyrus (MNI: 45,37,25) and right inferior parietal lobule (MNI: 43, À45, 44) under F4 and P4 electrodes, respectively (Fig. 2).The coordinates for these regions were taken from a metaanalysis study by Wang et al. (2019).These coordinates were in Talairach format and thus required conversion to MNI, which was done by a transformation matrix (https:// www.brainmap.org/icbm2tal/tal2icbm_spm.m).

Electric field extraction
To extract the E-field intensities from the target regions, we adhered to the pipeline suggested by Evans et al. (2020).The ROAST structural MR and E-field images were processed using SPM12 with the following pipeline: structural and E-field images were resampled to 2 Â 2 Â 2 mm voxels and smoothed using a 4 mm full-width at halfmaximum Gaussian kernel.Following that, a binary mask of gray and white matter was created using the normalized-smoothed structural images to restrict voxel analysis inside the brain.The mask was created by averaging the individual nonbinary gray and white matter tissue masks generated by ROAST, with an inclusion threshold of .0.1.

Dose individualization
The doses were individualized for a target E-field intensity calculated by averaging the mean of the E-fields generated in the target brain regions across all the participants with a stimulation intensity of 1 mA.The stimulation intensity of 1 mA was used to limit the spatial distribution of the E-fields in nontarget regions.We obtained three target E-field intensities for the three targets.
The target E-field intensity for left M1 was 0.1135 6 0.021 V/m; for the right middle frontal gyrus and inferior parietal lobule, the target E-field intensities were 0.1267 6 0.034 and 0.0968 6 0.021 V/m (all the values rounded off to fourth decimal), respectively (Evans et al., 2020;Caulfield et al., 2022).The doses were then individualized using the following equation:

Individualized dose
Group average E-field ¼ 1 mA Dose E-field from 1 mA : Rearranging the equation, we get the following: Individualized dose ¼ 1 mA Dose Â Group average E-field E-field from 1 mA : For the tDCS experiment, individualizing stimulation doses was effective in generating uniform E-fields over the left M1 across the participants (i.e., stimulation was both individualized and dose controlled).In the tACS experiment, although we individualized the stimulation doses since there were two active electrodes, we could deliver dose-controlled stimulation only to one of the two sites.This is because the tES device we used could not deliver unequal intensities of current over two sites simultaneously.Hence, the stimulation dose was determined by the dose individualized over one of the electrodes.Thus, for the tACS experiment, although the stimulation was individualized, it was not completely dose controlled.Nevertheless, we expected that individualizing the dose over one of the electrodes would affect the E-field over the other site in a similar manner and therefore reduce variability.

tES
tES was delivered using Nurostym tES (Brainbox).The participants were blinded to the stimulation and underwent three sessions where they received a fixed dose of 1 mA, an individualized dose, or sham tES.The sessions were randomized across participants and scheduled at least 3 d apart.We calculated the individualized dose for tES using the reverse calculation approach described above.Sham stimulation involved a brief stimulation lasting 30 s with a 20 s ramp-up period, giving the impression of active stimulation without exerting any physiologically significant effects (Kessler et al., 2012).

tDCS
Saline-soaked pad type (7 Â 5 cm; area ¼ 35 cm 2 ) sponge electrodes were used for tDCS stimulation.The anode was placed over the C3 (left M1), and the cathode was placed over the Fp2 (contralateral forehead; Nitsche et al., 2003;Fig. 3A).The longer axis of the electrode was placed along the mediolateral direction.The impedance was kept ,20 kX during the stimulation with the electrodes held in position using rubber headbands.The stimulation lasted for the entire session (through blocks 1-8), ;17 6 1.95 min.For the fixed dose session, subjects received continuous stimulation of 1 mA with a 20 s ramp-up period at the start of the experiment.Individualized dose sessions had a unique current dose with a 20 s ramp-up period for each participant.In contrast, in sham sessions, participants received stimulation for 30 s at the start of the experiment, which did not affect performance.

tACS
Three ring-shaped (inner radius ¼ 12 mm; outer radius ¼ 24 mm) rubber electrodes were used for the experiment.Anode electrodes were placed on the 10-10 coordinates F4 (middle frontal gyrus) and P4 (inferior parietal lobule), and the cathode was placed over the T8 (temporal cortex; Fig. 3B).Each participant underwent three sessions of tACS stimulation with a stimulation frequency of 6.00 Hz and without DC offset (Violante et al., 2017).To keep the impedance ,20 kX and to maintain the electrodes in place, we used Ten20 (Weaver) conductive paste.The participants wore a mesh cap to further prevent electrodes from falling.Finally, the whole setup was held together using a rubber headband.For the fixed dose sessions, participants received a peak-to-peak amplitude of 1 mA with a 20 s ramp-up period at the start of the experiment.
The current amplitude varied in the individualized sessions.Two target intensities were obtained for calculating the individualized dose, one for each anode over 10-10 coordinates F4 and P4.We selected the higher of the two individualized doses for stimulation to ensure strong enough E-fields over both sites.Thus, it is important to note that although the tACS intensity was individualized, it was dose controlled only over one of the two sites.The stimulation lasted for the entire session, which was ;7.85 6 0.83 min.In sham sessions, participants received stimulation only for the initial 30 s of the experiment with 1 mA peak-to-peak amplitude.

Behavioral tasks
The participants performed the serial reaction time task (SRTT) that assesses implicit motor learning for the tDCS experiment (as described by Nitsche et al., 2003) and the 2-back task for the tACS experiment that assesses working memory (as described by Violante et al., 2017).
In the SRTT, the participants performed finger movements repeatedly without being aware of a sequential order.The task consisted of eight blocks; blocks 2-5 and 7-8 (learning blocks) had the same sequence to be performed repeatedly.In blocks 1 and 6 (random blocks), the sequence was random (Extended Data Fig. 1-1A).The difference between learning block 5 and random block 6 was taken as a measure of implicit learning.
The 2-back task consisted of a single block of 200 trials, with 50 target and 150 nontarget trials.The subjects were asked to register the response using their right index finger when the currently displayed number matched the one presented two trials earlier by pressing the left mouse button (Extended Data Fig. 1-1B).

Data analysis Serial reaction time task
We used the Nitsche et al. (2003) pipeline for the SRTT behavioral analysis (Nitsche et al., 2003).The reaction time (RT) was measured as the time from the presentation of the target to the time of response.The subject-wise analysis consisted of only the correctly responded trials; the incorrect trials and correct trials with RTs .3000or ,200 ms and trials with RT .3SDs of the individual's mean RT were discarded.A total of 933.3 6 17.53 of 960 trials was left for analysis.The mean RTs were normalized to that of the first block.The z scores showed two participants above and below the 3 SDs of the mean.Hence, they were excluded from further analysis.The normality test (Shapiro-Wilk test) revealed a non-normal distribution of data.Thus, we opted for a nonparametric two-way repeated-measures ANOVA for further analysis.The nonparametric ANOVA was performed using the ARTool package (Wobbrock et al., 2011), which implements the Aligned Rank Transform (ART) to conduct multifactorial nonparametric ANOVA while accounting for the repeated measures.The independent variables were block (eight levels) and stimulation condition (three levels; fixed, individualized, and sham).All blocks were normalized to block 1, which was considered as the individual baseline.Block 1 was included in the ANOVA so as to estimate the participants' performance relative to the baseline.Post hoc analysis was done using the ART contrast to perform multiple comparisons with Bonferroni adjustments (Elkin et al., 2021).

2-back task
The primary behavioral outcomes for this task were RT, accuracy, and d9 (a measure of sensitivity).The RT was measured as the time difference between the target onset and response.The accuracy was estimated as follows: where TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives.The accuracy was then converted to a percentage.In addition to RT and accuracy, we calculated d9, which takes into account the false alarm rate along with the hits to correct the response bias.The d9 was computed using the following formula: The Z denotes Z-transform.The hit rate and false alarm rate were calculated as follows: False alarm rate ¼ False alarm False alarm 1 Correct rejections : Next, to examine the effect of E-field intensity (V/m) and stimulation dose (mA) on performance, we computed Pearson's correlation between the RTs and the E-field intensity, and the RT and Stimulation dose.The E-field intensity was extracted from regions under 10-10 EEG coordinates F4 and P4.

Current flow modeling
For tDCS, the mean E-field generated over left M1 (MNI: À38, À20, 50) was 0.114 6 0.023 V/m (range, 0.061-0.154V/m) for 1 mA.This value was used as the target Efield intensity for calculating the individualized stimulation dose (Dose ¼ 1.045 6 0.30 mA; range ¼ 0.738-1.860mA).The variance in E-field intensity was reduced by 99.86% after dose individualization.Dose individualization normalized the individual E-field intensity to the target E-field intensity of 0.114 V/m (Fig. 4).Extended Data Fig. 4-1A describes the qualitative comparison of E-field intensity at left M1.
tACS applied to the frontoparietal cortices at a fixed dose of 1 mA (peak-to-peak amplitude) generated varying E-field intensities at the middle frontal gyrus (MNI: 45, 37, 25) and inferior parietal lobule (MNI: 43, À45, 44).The mean field intensities were 0.127 6 0.034 V/m (range ¼ 0.080-0.256V/m) and 0.097 6 0.021 V/m (range ¼ 0.057-0.161V/m) at the middle frontal gyrus and inferior parietal lobule, respectively, resulting in two different individualized doses.We used the higher of the two doses to stimulate both regions (Dose ¼ 1.219 6 0.193 mA; range ¼ 0.893-1.748mA).This caused the variance in E-field intensity (V/m) to reduce by 69.92% at the middle frontal gyrus, while it increased by 168.59% at the inferior parietal lobule (Fig. 5).Most importantly, this ensured that the E-field intensity over the stimulation sites was either equal to or greater than that generated by a fixed 1 mA current (Extended Data Fig. 4-1B describes the qualitative comparison of E-field intensity at frontoparietal cortices).To create homogeneous E-field intensity at the targeted cortical regions, the variability in stimulator output increased because of interindividual anatomic variances (Extended Data Fig. 4-2).
Next, we computed Pearson's correlation between the stimulation dose and RT across the blocks.Block 1 showed the maximum positive correlation (r (21) ¼ 0.705; p , 0.001) between the individualized dose and RT, indicating that the participants who received a higher dose of stimulation performed poorly in the task.We also found a trend in the correlation coefficient values across the blocks.There was a gradual reduction in Pearson's r from blocks 1-4, which became nonsignificant at block 5 (block 2: r (21) ¼ 0.619, p ¼ 0.003; block 3: r (21) ¼ 0.568, p ¼ 0.007; block 4: r (21) ¼ 0.488, p ¼ 0.025; block 5: r (21) ¼ 0.371, p ¼ 0.097).However, the correlation became positively significant in block 6 (r (21) ¼ 0.556; p ¼ 0.009), the random block, and then again turned nonsignificant in the successive learning blocks (Fig. 7).This trend indicates the effect of stimulation dose on implicit learning of the sequence.The correlation between E-field intensity (V/m) and RT (ms) did not show a significant relationship for any of the blocks (Extended Data Fig. 7-1).

Differential effect of stimulation intensity on performance
We did not find any difference in the variance in RT across the tDCS conditions.However, the performance in blocks 2-4 in the individualized dose condition showed a bimodal distribution, indicating a differential effect of stimulation on performance (Fig. 8, Extended Data Fig. 8-1).Based on this finding, we extended our analysis to explore the effect of stimulation intensity.For this, we divided the study subjects into the following two categories: those who received .1 mA current and those who Figure 6.The figure shows the performance across blocks for all the tDCS conditions.The y-axis represents absolute RT (ms) for A, whereas the RT (ms) is normalized to block 1 in B. R, Random block; L, learning block.A, The figure shows reduced RT for the stimulation conditions as compared with the sham condition.However, there is no significant difference between conditions as tested by ANOVA.The sham and the fixed dose conditions show the learning curve across blocks 2-5.B, The main effect of blocks and tDCS conditions was found, with individualized dose conditions significantly different from fixed dose and sham stimulation conditions.A trend of reducing RTs, reflecting implicit learning across learning blocks, is seen for the fixed dose and sham conditions.However, the individualized dose condition did not show this trend.Extended Data Figure 6-1 shows mean block-wise performance for all participants across tDCS sessions.
received ,1 mA current for individualized dose stimulation.This categorization is identical to that used by Caulfield et al. (2022) on the basis of a median of E-field intensity calculated from the dose-controlled method.There was a nearly equal distribution of participants; the cohort with stimulation intensity .1 mA had 10 participants, and the other cohort had 11 participants.
The two-way repeated-measures ANOVA (independent variables: block and tDCS condition) revealed significant main effects of block and tDCS condition for the ,1 mA group.However, the .1 mA cohort showed a significant main effect only of tDCS condition (F (2,207) ¼ 5.151; p , 0.007; h 2 ¼ 0.047; ANOVA by ART procedure) but not of block or a two-way interaction (Table 3).Interestingly, only the ,1 mA group showed the reduced RT trend across learning blocks (main effect of block: F (7,230) ¼ 4.690; p , 0.001; h 2 ¼ 0.125; ANOVA by ART procedure; Table 4), reflecting implicit learning.Here again, the reducing RT (ms) trend was observed only for the sham and fixed dose conditions (main effect of condition: F (2,230) ¼ 4.768; p ¼ 0.009; h 2 ¼ 0.03; ANOVA by ART procedure; Table 4).In contrast, the .1 mA group did not show the learning trend even for the sham and fixed dose conditions (Fig. 9A,B).Extended Data Figure 9-1A,B shows performance across blocks across tDCS conditions for the .1 and ,1 mA groups with absolute reaction time).Post hoc comparisons for the main effect of conditions showed a significant difference between the sham and  Further investigating these results, we compared the baseline performance of the two groups for sham condition block 1 (no active stimulation).We found a significant difference between the two groups; that is, the subjects in the ,1 mA group subjects had shorter RTs than those in the .1 mA subjects (.1mA::,1mA: t (17.148) ¼ 2.812; p ¼ 0.012; Cohen's d ¼ 1.235; Welch's test; Extended Data Fig. 9-2).Nevertheless, both groups showed similar outcomes in the individualized dose condition (i.e., without a learning trend).Extended Data Fig. 9-3, A and B, shows block-wise Pearson's correlation between the stimulation intensity (mA) and reaction time (ms) for the two cohorts.

tACS
We used RT, accuracy, and d' as the primary outcome measure of analysis (Fig. 10, Extended Data Fig. 10-1).We did not find any significant difference in performance across conditions for any of the behavioral outcomes.Also, the variability in performance showed no significant difference across the tACS conditions.
For the analysis of RTs, we considered only the correct responses for analyses.The fixed dose condition had the lowest RT (mean 6 SD ¼ 698.118 6 155.58 ms), while the individualized dose and the sham conditions had almost the same RTs (mean 6 SD; Sham, 716.264 6 166.59 ms; Individualized, 718.811 6 144.89 ms).The one-way ANOVA showed no significant difference in RT across conditions (Q (2) ¼ 0.4; p ¼ 0.819; Kendal's W ¼ 0.006; Friedman test; Fig. 10A, Extended Data Fig. 10-1A).

Discussion
In the current study, our findings show that although individualized dose tES reduces the variability in the E-field intensities at the targeted cortical surfaces considerably, it does not reduce the variability in behavioral outcomes.Moreover, we did not find a significant correlation between E-field intensity and behavioral performance in either of the behavioral tasks.These results do not support the hypothesis that physiological or behavioral effects relate linearly to the E-field intensities at a targeted cortical region.Other parameters may be equally relevant, including the current flow direction, sample size, the task performed during The variability in performance is similar across both conditions.For the individualized dose condition, the learning blocks 2, 3, and 4 showed a bimodal distribution, indicating a differential effect of stimulation on performance.Extended Data Figure 8-1 shows the distribution of RT for the sham condition.the session, the spatial distribution of E-field intensity, the number of stimulation sessions, or the polarization gradient across the cortical surface (Saucedo-Marquez et al., 2013;Jackson et al., 2016;Rawji et al., 2018;Biel et al., 2022).
tDCS Unfortunately, we could not replicate the behavioral results of past studies.In the tDCS experiment, we expected to see enhanced motor learning with fixed dose tDCS compared with the sham condition, which we did not observe.This further reiterates the issue of the inconsistent effect of tES protocols across different subject groups (Stagg et al., 2011;Wiethoff et al., 2014;Ambrus et al., 2016).Wiethoff et al. (2014) showed variability in motor evoked potentials in response to tDCS, with ;50% of the participants showing minor or no effects of stimulation and the remaining 50% showing a facilitatory effect with both anodal and cathodal stimulation.Further, implicit learning was evident in our subjects with both fixed dose and sham stimulation conditions, although this trend was not seen with individualized dose stimulation.One potential explanation could be that an individualized dose reduces the variance in the spatial distribution of the E-fields at the cortical site as well as across the brain (Evans et al., 2020), which might, in turn, affect the excitability of the other brain areas (small-scale and largescale networks) that could have indirectly affected the task performance (Nitsche, 2011;Yavari et al., 2017).
Probing into individual subject performances, 9 of 23 participants performed best in the sham stimulation and poorest in the individualized condition.These participants reported mild to moderate tES side effects such as tingling, itching, burning, and pain sensations in the feedback form, which could have affected their ability to concentrate on the task (Kessler et al., 2012).Another nine participants, however, showed improved task performance with the individualized dose, and, interestingly, they did not report any side effects from the stimulation.These findings suggest that the effectiveness of the individualized dose may be subject to chance, and that side effects need to be considered while designing tDCS protocols to enhance behavioral performances.Interestingly, we found a positive correlation between the individualized stimulation doses and RTs for different blocks, particularly in the randomized blocks, implying poor performance with increased stimulation dose.The gradual decrease in the correlation through the learning blocks could be because of the following two reasons: first, the participants may have learned the sequence implicitly and improved their performance; and second, they gradually became habituated to the stimulation sensation and performed better in the later blocks.Therefore, increasing stimulation intensity and E-field intensity does not necessarily correlate with improved behavioral performance.This has also been reported by others in the past (Esmaeilpour et al., 2018), implying that several factors other than current intensity influence behavioral outcomes.
Furthermore, analyzing the performance across tDCS sessions showed that participants performed significantly better in their first session than in subsequent sessions.Also, session 1 showed a clear learning trend indicating implicit motor learning across the blocks.However, since the order of the sham, fixed, and individualized tDCS conditions were randomized across participants, we think that the stronger learning effect seen in session 1 is unlikely to have influenced our results.Although studies claim that learning effects are minimal in these tasks (Willingham et al., 2000;Nitsche et al., 2003), our results show that they cannot be fully neglected.
In the exploratory post hoc analysis, the categorization of subjects based on stimulation intensity revealed by the bimodal distribution in performance in the individualized dose condition shows that only the participants from the ,1 mA group (N ¼ 11) showed the effect of learning, while the .1 mA group (N ¼ 10) did not.Additionally, individualizing the stimulation dose did not reduce the interindividual performance variability.Attributes such as anatomic characteristics, genetics, age, and architecture of local inhibitory and excitatory circuits each play a role in behavioral outcomes (Li et al., 2015).The striking disparity in the performance that we observed in our study might have resulted from a few or all of these variables or possibly because of the small sample size after classification.

tACS
In the working memory task, tACS did not significantly affect the performance for any of the outcome measures-  RT, accuracy, or d9 measure of sensitivity.The trielectrode montage used for stimulating frontoparietal cortices did not deliver a uniform E-field at the corresponding brain targets.The nonreproducibility of the tACS effect with the same electrode montage on frontoparietal stimulation has also been reported previously (Kleinert et al., 2017), and there could be multiple reasons for these results.There are mixed results regarding the kind of effects seen with the use of tACS for working memory.A study by Pahor and Jaušovec (2014) has reported differential effects of frontal and parietal stimulation for fluid intelligence, with parietal stimulation of tACS showing more pronounced effects.Additionally, the dual-site tACS causes unintended stimulation of the nontargeted brain region (here, temporal region), which might have a differential role in the behavioral task (Saturnino et al., 2017;Alekseichuk et al., 2019).Another reason could be that the task was too easy for the participants, as the same results were obtained for each stimulation condition; perhaps a more difficult task like the 3-back task would have been more suitable (Biel et al., 2022).It has previously been reported in several studies that the difference in performance is found only for subjects who are poor baseline performers (Tseng et al., 2012;Li et al., 2015).This illustration also corroborates previous fMRI literature on frontoparietal activation, where the frontal and parietal cortices showed robust BOLD activity only for hard::easy contrast across tasks (Fedorenko et al., 2013).Another reason could be that sham stimulation had a biologically meaningful effect (Fonteneau et al., 2019).Alternatively, participants could differentiate between active and sham stimulation (Greinacher et al., 2019).However, this is less likely to be the case as the stimulation intensity was 0.5 mA for each anode, and most participants did not report any side effects in the tES feedback form (e.g., itching, tingling, burning over the skin) from the tACS stimulation.Or simply, the anodal stimulation produced relatively weak E-fields in the targeted cortical region that could not produce a physiological effect (Datta et al., 2009).Nonetheless, individualizing the dose did not reduce the variability in behavioral performance.Perhaps, individualizing the dose parameter does not exclusively control the behavioral outcome.A recent study on animal models (Krause et al., 2022) reported that the baseline frequency preference of the targeted brain region determines the effect of tACS-the tACS frequency competes with the ongoing intrinsic neural oscillations to control spike timing.It first decreases the entrainment, and only at a higher stimulation amplitude does the possibility of the entrainment increase.Even a slight mismatch in the neural and stimulation frequency dramatically affects entrainment.Also, studies have found differential effects of slow and fast theta on working memory, with slow theta (4-4.5 Hz) but not fast theta (7 Hz) enhancing working memory (Bender et al., 2019;Jones et al., 2019).Another reason we could not find any significant difference between the tACS stimulation and the sham conditions could be the small sample size; the power analysis was computed assuming a larger expected effect size of at least 70 ms across tACS conditions.However, the mean difference between active and no stimulation conditions was merely 20 ms.
Thus, our findings in this study do not support a direct relationship between the E-field intensity and the behavioral output.tES affects both local and global brain networks causing neurochemical and neuroelectrical modulations in those regions; thus, contributing synergistically to the overall performance (Yavari et al., 2017).The targeted E-field (0.114 V/m) used for the tDCS experiment is relatively low compared with the E-fields (;0.185V/m) in studies by Evans et al. (2020) and Caulfield et al. (2022), which might have resulted because of low sample size (n ¼ 23) or because of the electrode montage used in the experiment.Typically, the brain regions between the target and the reference electrodes are affected by tES (Nasseri et al., 2015;Yavari et al., 2017).Although individualized dose stimulation reduces variability in the E-field intensity at the targeted region, it fails to limit the spatial distribution of the E-fields (Evans et al., 2020), leading to the stimulation of unintended brain regions.Moreover, the electrode montage significantly adds to the interindividual variability in the amount of radial inward current (Evans et al., 2022).Another major point of concern is that the trielectrode montage for the tACS experiment did not reduce the variability in the E-fields at the frontoparietal cortices, as expected.Therefore, this experiment may not have been literally dose controlled since the pipeline for dose individualization, proposed by Evans et al. (2022), is limited to bielectrode montages.However, we extended the same pipeline to replicate tACS effects on working memory using the trielectrode montage, as shown by Violante et al. (2017).In this attempt, although the variance of E-field intensity could not be effectively reduced over both active target regions, we were able to bring the E-fields above the threshold value of 0.1 V/m, thereupon ensuring that the absence of behavioral effects was not because of low E-field intensity (Caulfield et al., 2022).This clearly proved our assumption wrong by revealing that reducing variability in E-fields over one region may not necessarily impact E-fields over other brain regions similarly.Although we used the higher of the two doses required for stimulation, in 28 of 36 subjects, the dose for the frontal electrode was always higher and hence the dose was individualized based on the frontal electrode.This could be a reason for higher variance of E-field over the parietal electrode.An ideal way to have reduced variability would have been to use different stimulation intensities over the two sites, which was technically not possible with our system.Overall, this reiterates that optimizing the electrode montage for current spread and direction for each stimulation site becomes just as crucial.
In summary, individualizing the stimulation intensity did not reduce interindividual variability in behavioral performance.In this study, we investigated whether the pipeline proposed by Evans et al. (2020) for dose individualization reduces variance in participants' behavioral performance; the results do not support a linear relationship between the E-field and the behavioral performance.More robust analysis techniques like the Bayesian and machine learning models may provide more concrete evidence about the relationship between E-fields and tES efficacy.In accordance with recent studies, we suggest functional targeting using fMRI and tES to target the functionally relevant areas in a specific task (Jackson et al., 2016;Yavari et al., 2017;Esmaeilpour et al., 2020).In addition to the individualization of stimulation intensity (Esmaeilpour et al., 2018), the tES intervention must precede the optimization of other parameters like the number of stimulation sessions (Reis et al., 2009), the direction of current flow (Rawji et al., 2018;Evans et al., 2022), focality of the stimulation (Mikkonen et al., 2020), and baseline physiological and cognitive functions like attention (Antal et al., 2007;Biel et al., 2022) and performance ability (Tseng et al., 2012).Although dose individualization delivers a uniform E-field intensity accounting for the anatomic variability, its influence on the functional outcome in the brain is nonlinear and complex (Yavari et al., 2017).Moreover, the optimization of tES protocols to improve the effect size and clinical reproducibility necessitates a deeper understanding of the functional and physiological changes in the brain caused by tES.

Figure 3 .
Figure 3.The figure shows the ROAST processing steps.In the first step, the MRI undergoes the process of tissue segmentation.The subsequent steps include electrode placement, meshing, and finite element modeling.A, Electrode montage for the tDCS experiment.The anode (red) is positioned over 10-10 EEG coordinates C3 (Left M1), and the return (blue) is placed over the contralateral supraorbital region (Fp2).B, Electrode montage for the tACS experiment.The anodes (red) are positioned over the 10-10 EEG coordinates F4 (middle frontal gyrus) and P4 (Inferior parietal lobule), whereas the return is placed over T8 (temporal cortex).

Figure 4 .
Figure 4.The figure shows the distribution of E-field intensity in left M1 (MNI: À38, À20, 50) for fixed (green) and individualized doses (purple).The variance in E-field intensity was reduced by 99.86% after individualizing the dose.The individual E-field intensity for 1 mA is normalized to the mean E-field (0.114 V/m) following dosage individualization.Extended Data Figure 4-1, A and B, shows the quantitative comparison of the E-fields in the brain for fixed and individualized doses.

Figure 5 .
Figure 5.The E-field intensity distribution at frontoparietal cortices for fixed (green) and individualized (purple) doses.The variance in the distribution of E-field intensity at middle frontal gyrus (MNI: 45, 37, 25) was reduced by 69.92% after dose individualization.The selection of a higher individualized dose for targets F4 and P4 increased the variance at the inferior parietal lobule (MNI:43, À45, 44) by 168.59%.The higher individualized dose simultaneously stimulated both the targeted regions.

Figure 7 .
Figure7.Block-wise correlation between stimulation dose and performance.Pearson's r showed a positive correlation between stimulation dose and RTs.The randomized blocks (blocks 1 and 6, marked "R") showed a strong positive correlation, whereas the correlation coefficient across the learning blocks (block 2 to block 4) shows a reducing trend.Learning blocks 5, 7, and 8 do not show a significant correlation.The trend in correlation shows the effect of learning and stimulation doses, with the higher dose of stimulation hampering learning.Extended Data Figure7-1 shows block-wise correlation between E-field Intensity (V/m) and performance (ms).

Figure 8 .
Figure8.The plot shows the distribution of RT for every block across active tDCS conditions.R, Random block; L, learning block.The variability in performance is similar across both conditions.For the individualized dose condition, the learning blocks 2, 3, and 4 showed a bimodal distribution, indicating a differential effect of stimulation on performance.Extended Data Figure8-1 shows the distribution of RT for the sham condition.

Figure 9 .
Figure9.A, B, The plots show mean RT (normalized) across blocks for different tDCS conditions for the .1 mA (A) and ,1 mA (B) subject groups.R, Random block; L, learning block.The RTs across blocks are normalized to block 1.The main effect of blocks is reflected by the implicit learning trend for the sham and fixed dose conditions in the ,1 mA group.This indicates that the implicit learning of the ,1 mA group is better than the other group.Extended Data Figure9-1 displays mean block-wise performance [absolute RT (ms)] across tDCS conditions.Extended Data Figure9-2 shows the baseline performance across .1 and ,1 mA groups.For block-wise correlation between the stimulation intensity and performance for the .1 and ,1 mA groups, refer to Extended Data Figure9-3.

Figure 10 .
Figure 10.Behavioral performance across fixed and individualized dose conditions for the tACS experiment.A, RT.B, Accuracy.C, d9.To review the distribution of RT (ms) across the sham condition, refer to Extended Data Figure 10-1.

Table 1 :
Power analysis table

Table 3 :
Results of 2-factorial repeated-measures ANOVA for the differential effects of blocks and tDCS conditions for the .1 mA group **p , 0.01.edom;df, degree of freedom, degree of freedom residuals; h 2 , eta squared (partial).